1. Field Of The Invention
The present invention relates to artificial neural networks. More particularly, the present invention relates to an array of electrically-adaptable synapses for use in a neural network.
2. The Prior Art
Several schemes for using a matrix of electronic devices for neural network applications have been proposed. To date, all such schemes involve using "weights" to control the amount of current injected into an electrical node "neuron". In most prior art structures, these weights were set by controlling the value of a resistor or the saturation current of a transistor. The limitation of any such scheme is that the value of any parameter of an electronic device in an integrated circuit is not well controlled. For example, the saturation currents of two MOS transistors of the same size can differ by a factor of two if these devices are operated in the sub-threshold regime. The "training" mechanism that adjusts the weights must take these uncertainties into account by iterating and testing the outcome of the weight adjustments process.
U.S. Pat. No. 5,083,044 discloses and claims a synaptic element comprising an adaptive amplifier. The amplifier incorporates a floating gate element and may be adapted by exposing a portion of the floating gate of the adaptive amplifier to a source of ultraviolet light. This synaptic element may be used as a trainable synapse in which the weights may be adjusted to compensate for typical transistor nonuniformities and to otherwise manipulate the weights.
It is desirable to provide an adaptive mechanism which may be adapted by electrical means whereby the amplifier electrically adjusts itself to any uncertainty in device parameters, as part of the training process. Such a synaptic element is disclosed in parent application Ser. No. 07/525,764, filed May 18, 1990, now U.S. Pat. No. 5,059,920. This synaptic element has the advantage that it may be electrically adapted while the circuit is in its normal operating regime. The present application extends the disclosure of the parent application to include an electrically-trainable neural network.